JsonLLM provides an efficient solution for structured data extraction and API generation. By utilizing JSON schemas, users can automatically format data in a consistent structure, ensuring reliable integrations and data transfers. It's designed for those who require precise data manipulation and want to enhance their outputs with structured formats. Perfect for transforming unstructured data into valuable, usable JSON output, JsonLLM helps in reducing the complexity of creating APIs and working with large datasets.

LLM Functions provides a simple framework to bridge large language model responses with real code execution. You define functions via JSON schemas, register them with the library, and the LLM will return structured function calls when appropriate. The library parses those responses, validates the parameters, and invokes the correct handler. It supports synchronous and asynchronous callbacks, custom error handling, and plugin extensions, making it ideal for applications that require dynamic data lookup, external API calls, or complex business logic within AI-driven conversations.

OpenAgentSpec defines a comprehensive set of JSON schemas, API interfaces, and protocol guidelines for AI agents. It covers agent registration, capability declaration, messaging formats, event handling, memory management, and extension mechanisms. By following the spec, organizations can create agents that communicate reliably with each other and with host environments, reducing integration effort and fostering a reusable ecosystem of interoperable AI components.

UIM Protocol defines a standardized JSON schema through which AI agents can describe user interface elements, behaviors and events. It covers components such as buttons, input fields, forms, tables, trees and charts, and supports event hooks for user interactions. Frontend renderers consume UIM messages to build and update interfaces on the fly without manual UI coding. Versioned message envelopes ensure backward compatibility. By leveraging UIM Protocol, teams can iterate on conversational assistants and data dashboards faster, maintain consistent UX patterns across channels, and decouple AI decision logic from presentation layers.

Xemantic AI Tool Schema is a set of JSON Schema and TypeScript type definitions designed to standardize the way AI agent tools are described, validated, and invoked. Developers can define tool metadata such as name, description, and parameters, then validate instances against the schema or use generated TypeScript interfaces during development. The schema supports parameter types, nested structures, default values, and version control, ensuring robust validation and compatibility. By following a consistent schema, AI Agents can discover and call tools reliably at runtime, improving maintainability and reducing integration errors. The package integrates seamlessly with Xemantic AI Agents and can be extended for custom use cases.